I work in AI and Computer Vision, which means I spend my days teaching machines to make sense of the visual world. Then I go home and try to do the same thing with my own life, with significantly worse results.

I’m based in Brașov, Romania. I work at Dynamic Ventures Inc. (d/b/a CountThings), where I spend most of my time training computer vision models — deep neural networks built on human-labeled data, because that’s what reliably solves real problems at scale. But it’s not the question I find most interesting.

What keeps pulling me back is a different one: what would it take to build systems that don’t need all those labels? Systems that learn by observing the world, by predicting what’s missing or what comes next — the way humans and animals seem to learn, through exposure first and explicit feedback much later. That belief — that real intelligence starts with perception and prediction, not with memorization or rules — is what I’ve been circling around for years, and it’s the idea behind AVM Innovation, my own initiative exploring how we design software and AI when we stop treating data as ground truth and start treating it as what it actually is: a partial, instrument-shaped projection of a world we can only make sense of by learning to anticipate its gaps.

I’m also involved in Povești Care Cresc, a children’s book project my best friend Vlad started. I help where I can, because I think critical thinking and thoughtful education are the quiet foundations of adaptable generations, and because books that take children seriously are rarer than they should be. From time to time I also support Asociația În Numele lui Alexandru, a non-profit founded by the parents of my childhood friend Alexandru, whom we lost in the Colectiv nightclub fire in Bucharest. Together, in small and imperfect ways, we try to help projects that push for a more responsible, less indifferent society.

I’ve always been drawn to the way curiosity works when you let it cross borders. The same patterns keep appearing in completely unrelated fields — physicists, psychologists, biologists, machine learning engineers, all rediscovering each other’s concepts and giving them new names, apparently without talking to each other. I found that both fascinating and slightly ridiculous. Lenses is where I try to map those patterns — not as an expert, but as someone who reads across fields and can’t stop noticing the overlaps.

I’m not an academic. I’m not a guru. I’m an engineer who reads too much, connects things that probably shouldn’t be connected, and has a persistent suspicion that the universe is running on fewer subroutines than it wants us to believe. I treat most explanations with the kind of polite skepticism that tends to annoy people who are very sure of themselves.

A few things worth knowing, before you go further:

• I don’t offer answers. I offer perspectives — a small, arbitrary subset of the infinite number one could construct on any given topic. I’m aware of this. I’ve simply chosen not to write all of them, partly for practical reasons, partly out of respect for the finite human lifespan.
• Every piece is versioned. It’s a snapshot of how I understand something at the time of writing — which means it will change, get contradicted by something I read next Tuesday, or quietly embarrass me in three years. That’s fine. The alternative is pretending I’ve figured it all out, and we both know how that usually ends.
• I grew up Orthodox and no longer believe in most of it. I still say “God help me” when things get difficult, which is either hypocritical or human — I’ve decided it’s probably both. Most of my conclusions end this way.

I write these pieces as a way of thinking in public. They’re my own way of making sense of the world, and I’ve simply decided to make that process visible. If something here resonates or you see a perspective I missed, and you will, I’d like to hear it.